mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-28 13:35:43 +08:00
Eddie Hebert
383 lines
13 KiB
Python
383 lines
13 KiB
Python
#
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# Copyright 2013 Quantopian, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from collections import deque
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import pytz
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import numpy as np
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import pandas as pd
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from datetime import timedelta, datetime
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from unittest import TestCase
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from zipline import ndict
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from zipline.utils.test_utils import setup_logger
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from zipline.sources import SpecificEquityTrades
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from zipline.transforms.utils import StatefulTransform, EventWindow
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from zipline.transforms import MovingVWAP
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from zipline.transforms import MovingAverage
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from zipline.transforms import MovingStandardDev
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from zipline.transforms import Returns
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import zipline.utils.factory as factory
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from zipline.test_algorithms import BatchTransformAlgorithm
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def to_dt(msg):
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return ndict({'dt': msg})
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class NoopEventWindow(EventWindow):
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"""
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A no-op EventWindow subclass for testing the base EventWindow logic.
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Keeps lists of all added and dropped events.
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"""
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def __init__(self, market_aware, days, delta):
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EventWindow.__init__(self, market_aware, days, delta)
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self.added = []
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self.removed = []
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def handle_add(self, event):
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self.added.append(event)
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def handle_remove(self, event):
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self.removed.append(event)
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class TestEventWindow(TestCase):
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def setUp(self):
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self.sim_params = factory.create_simulation_parameters()
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setup_logger(self)
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self.monday = datetime(2012, 7, 9, 16, tzinfo=pytz.utc)
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self.eleven_normal_days = [self.monday + i * timedelta(days=1)
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for i in xrange(11)]
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# Modify the end of the period slightly to exercise the
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# incomplete day logic.
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self.eleven_normal_days[-1] -= timedelta(minutes=1)
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self.eleven_normal_days.append(self.monday +
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timedelta(days=11, seconds=1))
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# Second set of dates to test holiday handling.
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self.jul4_monday = datetime(2012, 7, 2, 16, tzinfo=pytz.utc)
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self.week_of_jul4 = [self.jul4_monday + i * timedelta(days=1)
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for i in xrange(5)]
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def test_market_aware_window_normal_week(self):
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window = NoopEventWindow(
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market_aware=True,
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delta=None,
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days=3
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)
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events = [to_dt(date) for date in self.eleven_normal_days]
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lengths = []
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# Run the events.
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for event in events:
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window.update(event)
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# Record the length of the window after each event.
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lengths.append(len(window.ticks))
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# The window stretches out during the weekend because we wait
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# to drop events until the weekend ends. The last window is
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# briefly longer because it doesn't complete a full day. The
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# window then shrinks once the day completes
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self.assertEquals(lengths, [1, 2, 3, 3, 3, 4, 5, 5, 5, 3, 4, 3])
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self.assertEquals(window.added, events)
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self.assertEquals(window.removed, events[:-3])
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def test_market_aware_window_holiday(self):
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window = NoopEventWindow(
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market_aware=True,
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delta=None,
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days=2
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)
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events = [to_dt(date) for date in self.week_of_jul4]
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lengths = []
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# Run the events.
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for event in events:
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window.update(event)
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# Record the length of the window after each event.
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lengths.append(len(window.ticks))
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self.assertEquals(lengths, [1, 2, 3, 3, 2])
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self.assertEquals(window.added, events)
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self.assertEquals(window.removed, events[:-2])
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def tearDown(self):
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setup_logger(self)
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class TestFinanceTransforms(TestCase):
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def setUp(self):
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self.sim_params = factory.create_simulation_parameters()
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setup_logger(self)
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trade_history = factory.create_trade_history(
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133,
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[10.0, 10.0, 11.0, 11.0],
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[100, 100, 100, 300],
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timedelta(days=1),
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self.sim_params
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)
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self.source = SpecificEquityTrades(event_list=trade_history)
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def tearDown(self):
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self.log_handler.pop_application()
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def test_vwap(self):
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vwap = MovingVWAP(
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market_aware=True,
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window_length=2
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)
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transformed = list(vwap.transform(self.source))
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# Output values
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tnfm_vals = [message[vwap.get_hash()] for message in transformed]
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# "Hand calculated" values.
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expected = [
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(10.0 * 100) / 100.0,
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((10.0 * 100) + (10.0 * 100)) / (200.0),
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# We should drop the first event here.
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((10.0 * 100) + (11.0 * 100)) / (200.0),
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# We should drop the second event here.
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((11.0 * 100) + (11.0 * 300)) / (400.0)
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]
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# Output should match the expected.
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self.assertEquals(tnfm_vals, expected)
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def test_returns(self):
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# Daily returns.
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returns = Returns(1)
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transformed = list(returns.transform(self.source))
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tnfm_vals = [message[returns.get_hash()] for message in transformed]
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# No returns for the first event because we don't have a
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# previous close.
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expected = [0.0, 0.0, 0.1, 0.0]
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self.assertEquals(tnfm_vals, expected)
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# Two-day returns. An extra kink here is that the
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# factory will automatically skip a weekend for the
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# last event. Results shouldn't notice this blip.
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trade_history = factory.create_trade_history(
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133,
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[10.0, 15.0, 13.0, 12.0, 13.0],
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[100, 100, 100, 300, 100],
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timedelta(days=1),
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self.sim_params
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)
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self.source = SpecificEquityTrades(event_list=trade_history)
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returns = StatefulTransform(Returns, 2)
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transformed = list(returns.transform(self.source))
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tnfm_vals = [message[returns.get_hash()] for message in transformed]
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expected = [
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0.0,
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0.0,
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(13.0 - 10.0) / 10.0,
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(12.0 - 15.0) / 15.0,
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(13.0 - 13.0) / 13.0
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]
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self.assertEquals(tnfm_vals, expected)
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def test_moving_average(self):
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mavg = MovingAverage(
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market_aware=True,
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fields=['price', 'volume'],
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window_length=2
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)
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transformed = list(mavg.transform(self.source))
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# Output values.
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tnfm_prices = [message[mavg.get_hash()].price
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for message in transformed]
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tnfm_volumes = [message[mavg.get_hash()].volume
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for message in transformed]
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# "Hand-calculated" values
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expected_prices = [
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((10.0) / 1.0),
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((10.0 + 10.0) / 2.0),
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# First event should get dropped here.
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((10.0 + 11.0) / 2.0),
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# Second event should get dropped here.
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((11.0 + 11.0) / 2.0)
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]
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expected_volumes = [
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((100.0) / 1.0),
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((100.0 + 100.0) / 2.0),
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# First event should get dropped here.
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((100.0 + 100.0) / 2.0),
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# Second event should get dropped here.
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((100.0 + 300.0) / 2.0)
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]
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self.assertEquals(tnfm_prices, expected_prices)
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self.assertEquals(tnfm_volumes, expected_volumes)
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def test_moving_stddev(self):
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trade_history = factory.create_trade_history(
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133,
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[10.0, 15.0, 13.0, 12.0],
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[100, 100, 100, 100],
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timedelta(days=1),
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self.sim_params
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)
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stddev = MovingStandardDev(
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market_aware=True,
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window_length=3,
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)
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self.source = SpecificEquityTrades(event_list=trade_history)
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transformed = list(stddev.transform(self.source))
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vals = [message[stddev.get_hash()] for message in transformed]
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expected = [
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None,
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np.std([10.0, 15.0], ddof=1),
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np.std([10.0, 15.0, 13.0], ddof=1),
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np.std([15.0, 13.0, 12.0], ddof=1),
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]
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# np has odd rounding behavior, cf.
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# http://docs.scipy.org/doc/np/reference/generated/np.std.html
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for v1, v2 in zip(vals, expected):
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if v1 is None:
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self.assertIsNone(v2)
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continue
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self.assertEquals(round(v1, 5), round(v2, 5))
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############################################################
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# Test BatchTransform
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class TestBatchTransform(TestCase):
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def setUp(self):
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self.sim_params = factory.create_simulation_parameters(
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start=datetime(1990, 1, 1, tzinfo=pytz.utc),
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end=datetime(1990, 1, 8, tzinfo=pytz.utc)
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)
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setup_logger(self)
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self.source, self.df = \
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factory.create_test_df_source(self.sim_params)
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def test_event_window(self):
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algo = BatchTransformAlgorithm(sim_params=self.sim_params)
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algo.run(self.source)
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wl = algo.window_length
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# The following assertion depend on window length of 3
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self.assertEqual(wl, 3)
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self.assertEqual(algo.history_return_price_class[:wl],
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[None] * wl,
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"First three iterations should return None." + "\n" +
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"i.e. no returned values until window is full'" +
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"%s" % (algo.history_return_price_class,))
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self.assertEqual(algo.history_return_price_decorator[:wl],
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[None] * wl,
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"First three iterations should return None." + "\n" +
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"i.e. no returned values until window is full'" +
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"%s" % (algo.history_return_price_decorator,))
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# After three Nones, the next value should be a data frame
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self.assertTrue(isinstance(
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algo.history_return_price_class[wl],
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pd.DataFrame)
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)
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# Test whether arbitrary fields can be added to datapanel
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field = algo.history_return_arbitrary_fields[-1]
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self.assertTrue(
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'arbitrary' in field.items,
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'datapanel should contain column arbitrary'
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)
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self.assertTrue(all(
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field['arbitrary'].values.flatten() ==
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[123] * algo.window_length),
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'arbitrary dataframe should contain only "test"'
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)
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for data in algo.history_return_sid_filter[wl:]:
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self.assertIn(0, data.columns)
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self.assertNotIn(1, data.columns)
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for data in algo.history_return_field_filter[wl:]:
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self.assertIn('price', data.items)
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self.assertNotIn('ignore', data.items)
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for data in algo.history_return_field_no_filter[wl:]:
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self.assertIn('price', data.items)
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self.assertIn('ignore', data.items)
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for data in algo.history_return_ticks[wl:]:
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self.assertTrue(isinstance(data, deque))
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for data in algo.history_return_not_full:
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self.assertIsNot(data, None)
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# test overloaded class
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for test_history in [algo.history_return_price_class,
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algo.history_return_price_decorator]:
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# starting at window length, the window should contain
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# consecutive (of window length) numbers up till the end.
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for i in range(algo.window_length, len(test_history)):
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np.testing.assert_array_equal(
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range(i - algo.window_length + 1, i + 1),
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test_history[i].values.flatten()
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)
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def test_passing_of_args(self):
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algo = BatchTransformAlgorithm(1,
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kwarg='str', sim_params=self.sim_params)
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self.assertEqual(algo.args, (1,))
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self.assertEqual(algo.kwargs, {'kwarg': 'str'})
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algo.run(self.source)
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expected_item = ((1, ), {'kwarg': 'str'})
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self.assertEqual(
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algo.history_return_args,
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[
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# 1990-01-01 - market holiday, no event
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# 1990-01-02 - window not full
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None,
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# 1990-01-03 - window not full
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None,
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# 1990-01-04 - window not full, 3rd event
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None,
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# 1990-01-05 - window now full
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expected_item,
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# 1990-01-08 - window now full
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expected_item
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])
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